Wan 2 7

Wan 2.7 — Pro Pack on RunComfy

runcomfy.com · Text-to-video · GitHub

Wan-AI's Wan 2.7 — flagship video model with multi-reference conditioning and audio-driven lip-sync — hosted on the RunComfy Model API.

npx skills add agentspace-so/runcomfy-skills --skill wan-2-7 -g

When to pick this model (vs siblings)

You want	Use
Lip-sync video to an audio track you supply	Wan 2.7 (audio_url)
Multi-reference fine motion control	Wan 2.7
Smooth transitions, accurate motion physics	Wan 2.7
Currently-#1 blind-vote video model	HappyHorse 1.0
Multi-modal cinematic with image+video+audio refs + in-pass voice generation	Seedance 2.0 Pro
Cinematic motion editing on existing footage	Kling Video O1
Ultra-fast iteration	LTX 2

If the user said "Wan" / "Wan 2.7" / "wan-ai" / "alibaba video" explicitly, route here regardless.

Prerequisites

1. RunComfy CLI — npm i -g @runcomfy/cli
2. RunComfy account — runcomfy login opens a browser device-code flow.
3. CI / containers — set RUNCOMFY_TOKEN=<token> instead of runcomfy login.

Endpoints + input schema

wan-ai/wan-2-7/text-to-video

Field	Type	Required	Default	Notes
prompt	string	yes	—	Up to ~5000 chars / ~1500 tokens.
audio_url	string	no	—	WAV/MP3, 3–30s, ≤15MB. Drives lip-sync. Omit → background music auto-generated.
aspect_ratio	enum	no	16:9	16:9, 9:16, 1:1, 4:3, 3:4.
resolution	enum	no	1080p	720p or 1080p.
duration	enum	no	5	2–15 (whole seconds).
negative_prompt	string	no	—	Up to 500 chars. Concrete issues to avoid.
enable_prompt_expansion	bool	no	true	Auto-rewrites short prompts. Disable for literal control.
seed	int	no	—	0..2^31-1. Reuse for variants.

How to invoke

Default (5s 1080p 16:9, prompt-expanded):

runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{"prompt": "<user prompt>"}' \
  --output-dir <absolute/path>

Audio-driven lip-sync (your own track):

runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{
    "prompt": "Medium close-up of the spokesperson, warm key light, locked tripod, slight breathing motion.",
    "audio_url": "https://.../voiceover.mp3",
    "duration": 12,
    "aspect_ratio": "9:16"
  }' \
  --output-dir <absolute/path>

Literal control (no auto-expansion):

runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{
    "prompt": "<exactly what you want, verbatim>",
    "enable_prompt_expansion": false,
    "negative_prompt": "no subtitles, no flicker, no distorted hands"
  }' \
  --output-dir <absolute/path>

Prompting — what actually works

Camera + motion in plain English. "Slow dolly in", "locked tripod, low angle", "handheld follow", "crane move from above". Front-load the shot.

One primary action per clip. Don't pile up multiple competing actions. Pick the beat: "she turns, then smiles" not "she turns AND smiles AND a bus passes AND...".

Use negative_prompt for concrete issues. Good: "no subtitles, no watermark, no flicker". Bad (vague): "no bad lighting".

Prompt expansion is on by default. Short prompts get auto-rewritten by the model. For terse / literal prompts (e.g. brand-strict ad copy), disable with enable_prompt_expansion: false.

Audio specs matter. audio_url must be 3–30s, ≤15MB, WAV/MP3. Out-of-range files reject. Match audio length to clip duration.

Iterate seeds. Reuse the same seed when you want consistent output across variants of the same prompt. Change seed for genuine variety.

Anti-patterns:

• Static-frame descriptions → motion will be vague.
• Vague negatives ("no bad colors") → ignored.
• Audio outside the 3–30s / 15MB / WAV-MP3 spec → rejected.
• Prompts > 5000 chars / 1500 tokens → degraded output.

Where it shines

Use case	Why Wan 2.7
Lip-synced ads with custom voiceover	audio_url accepts your track
Multi-language dub variants	Same prompt, different audio_url per language
Multi-reference motion control	Up to 5 reference media (image / video / voice)
Smooth transitions + motion physics	Strong physics-aware motion priors
Negative-prompted clean output	Targeted issue exclusion

Sample prompts (verified to produce strong results)

Page example (product showcase):

Cinematic medium shot of a product on a marble surface, soft studio
lighting, slow subtle camera push-in, shallow depth of field, premium
commercial look, crisp 1080p detail

Lip-synced spokesperson (with audio_url):

Medium close-up of a confident spokesperson in a softly-lit recording
booth, leaning slightly toward the camera, locked tripod, shallow depth
of field, warm key light from camera-left.

Vertical platform-native:

9:16 vertical short. A barista pulls a single espresso shot, steam
rising into morning sun, rich crema slowly forming. Close-up handheld,
shallow DOF, warm cafe ambience.

Limitations

• Duration cap 15s. For longer narratives, stitch multiple calls.
• No native 4K — 1080p ceiling.
• Aspect ratios — only the 5 documented values.
• Audio specs — 3–30s, ≤15MB, WAV/MP3 only.
• Reference media cap 5 (image + video + voice combined).
• For in-pass voice generation (no separate audio track), use Seedance 2.0 Pro — Wan accepts audio rather than generating it.

Exit codes

code	meaning
0	success
64	bad CLI args
65	bad input JSON / schema mismatch
69	upstream 5xx
75	retryable: timeout / 429
77	not signed in or token rejected

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill invokes runcomfy run wan-ai/wan-2-7/text-to-video with a JSON body matching the schema. The CLI POSTs to https://model-api.runcomfy.net/v1/models/wan-ai/wan-2-7/text-to-video, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

• Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.
• Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.
• Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.
• Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.
• Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.